公开(公告)号：US20220327371A1

公开(公告)日：2022-10-13

申请号：US17616983

申请日：2020-06-05

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Jingxi Li ,  Deniz Mengu ,  Yi Luo

IPC: G06N3/067 ,  G06N3/08 ,  G06N20/00 ,  G02B27/42

Abstract: A diffractive optical neural network device includes a plurality of diffractive substrate layers arranged in an optical path. The substrate layers are formed with physical features across surfaces thereof that collectively define a trained mapping function between an optical input and an optical output. A plurality of groups of optical sensors are configured to sense and detect the optical output, wherein each group of optical sensors has at least one optical sensor configured to capture a positive signal from the optical output and at least one optical sensor configured to capture a negative signal from the optical output. Circuitry and/or computer software receives signals or data from the optical sensors and identifies a group of optical sensors in which a normalized differential signal calculated from the positive and negative optical sensors within each group is the largest or the smallest of among all the groups.

公开(公告)号：US20240290473A1

公开(公告)日：2024-08-29

申请号：US18572113

申请日：2022-06-29

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA ,  UNITED STATES GOVERNMENT AS REPRESENTED BY THE DEPARTMENT OF VETERANS AFFAIRS

Inventor： Aydogan Ozcan ,  Jingxi Li ,  Yair Rivenson ,  Xiaoran Zhang ,  Philip O. Scumpia ,  Jason Garfinkel ,  Gennady Rubinstein

IPC: G16H30/40 ,  A61B5/00 ,  G06T7/00 ,  G06T15/08

CPC classification number: G16H30/40 ,  A61B5/0068 ,  A61B5/0071 ,  G06T7/0012 ,  G06T15/08 ,  G06T2207/20084 ,  G06T2207/30088

Abstract: A deep learning-based system and method is provided that uses a convolutional neural network to rapidly transform in vivo reflectance confocal microscopy (RCM) images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of epidermis, dermal-epidermal junction, and superficial dermis layers. The network is trained using ex vivo RCM images of excised unstained tissue and microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. The trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features of traditional histology from the same excised tissue. The system and method enables more rapid diagnosis of malignant skin neoplasms and reduces invasive skin biopsies.

公开(公告)号：US20230153600A1

公开(公告)日：2023-05-18

申请号：US17920774

申请日：2021-05-04

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Jingxi Li ,  Deniz Mengu ,  Yair Rivenson

IPC: G06N3/067 ,  G02B27/42

CPC classification number: G06N3/067 ,  G02B27/4272

Abstract: A machine vision task, machine learning task, and/or classification of objects is performed using a diffractive optical neural network device. Light from objects passes through or reflects off the diffractive optical neural network device formed by multiple substrate layers. The diffractive optical neural network device defines a trained function between an input optical signal from the object light illuminated at a plurality or a continuum of wavelengths and an output optical signal corresponding to one or more unique wavelengths or sets of wavelengths assigned to represent distinct data classes or object types/classes created by optical diffraction and/or reflection through/off the substrate layers. Output light is captured with detector(s) that generate a signal or data that comprise the one or more unique wavelengths or sets of wavelengths assigned to represent distinct data classes or object types or object classes which are used to perform the task or classification.